Self-supervised learning has been proven to be effective for learning representative features in computer vision [1]}, [2]} and NLP [3]}, [4]}. However, there is very little research about self-supervised learning for large-scale point clouds. In particular, the simple masked autoencoders (MAE) [1]} have been proved effective in learning representative features with the masked data reconstruction task. Similar to 2D images and videos, the large-scale point clouds are also information redundant data, thus designing the MAE for pre-training point clouds is also feasible. Unlike the 2D images or languages, the large amounts of point clouds in autonomous driving are hard to be reconstructed with neural networks.
